
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "40"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 75 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 71 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	21.08%	2.151140	-0.677221	0.007973	1.343509	0.213063	-1.051770	-0.224992	0.571496	0.7689	0.7615	0.2436	5.019465	4.747223
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	11.83%	1.052328	-0.596626	-0.045422	2.219851	-0.262333	-0.800382	-0.859277	-0.508778	0.6911	0.6782	0.2264	2.527632	2.675266
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.622360	3.818806	38.318756	37.719403	29.245198	35.271501	0.800486	3.634136	0.6408	0.6544	0.4304	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	1.08%	1.120515	0.392475	0.593469	2.562383	0.085457	-0.672838	-0.718151	-0.153295	0.5941	0.5971	0.3358	1.709732	1.636814
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.706159	0.807076	0.814518	2.570392	0.511177	-0.792883	-0.478495	0.352857	0.5966	0.5962	0.3287	1.770965	1.687706
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	26.88%	1.313843	0.684660	0.956772	2.429969	-0.358389	-0.677487	-0.387360	0.348776	0.6702	0.6518	0.2459	2.649782	2.504749
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	8.06%	1.352023	-0.913120	-0.096741	2.420686	0.056208	-1.053926	-0.733940	-0.308202	0.7469	0.7233	0.2112	5.543415	4.415483
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	1.459078	0.453531	-0.199230	2.161337	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.370068	2.685420	3.854223	2.611197	6.021315	6.519948	1.051257	0.134390	0.6072	0.6124	0.3180	5.052610	4.773398
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	36.522800	36.756315	13.968751	14.500300	2.999970	4.193927	5.619626	7.732691	0.5817	0.5841	0.3365	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.299302	3.966288	1.363871	6.673118	11.997854	36.835626	30.593529	32.971863	0.5578	0.5574	0.3301	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.626746	1.332934	2.620659	9.840195	0.339707	-0.250666	-0.606158	-0.162341	0.5998	0.5919	0.3456	6.041297	5.886039
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.553872	1.282690	2.311823	9.074191	0.104479	-0.291913	-0.601790	-0.170408	0.5901	0.5791	0.3485	4.351876	4.013045
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.265429	1.922647	3.200159	9.820327	0.018129	-0.287412	-0.418630	0.335036	0.6092	0.6000	0.3434	5.645339	4.544934
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.725183	1.429295	2.669935	10.152204	0.405735	-0.344977	-0.834975	0.618421	0.6021	0.6006	0.3415	7.401908	6.517217
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.798475	1.672385	3.366246	12.087288	0.090886	-0.363792	-0.152087	0.211594	0.5962	0.5935	0.3448	6.263473	4.830302
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	5.521947	4.902935	18.219838	13.766438	37.301555	57.387994	28.708846	61.529230	0.6686	0.6768	0.4461	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.945086	1.764153	2.852815	9.315018	0.499770	-0.035524	-0.564440	0.568538	0.6069	0.6023	0.3399	6.071772	4.716928
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.935168	1.620334	2.421259	7.630543	0.892136	1.674824	-0.046693	1.048528	0.6131	0.6079	0.3458	6.036808	5.142641
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.902757	1.371828	2.000225	7.231227	1.330752	0.998094	-0.383897	-0.030876	0.6175	0.6119	0.3397	4.624478	4.040501
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.139924	1.281238	3.368760	12.451816	-0.023810	-0.260182	-0.411591	0.291067	0.6077	0.6008	0.3507	8.591959	1.978826
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.861153	1.372745	2.035814	7.256126	0.188628	-0.059775	0.314701	0.661336	0.6114	0.6056	0.3475	5.319530	4.666660
2459571	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.775034	4.693406	19.876840	15.353280	101.294709	120.041918	49.326737	115.066746	0.6224	0.6270	0.3750	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	0.00%	-1.068338	-1.643548	-0.959156	-1.910697	-1.007441	-1.247972	-1.024313	-0.346174	0.6149	0.6268	0.3309	1.365955	1.128799
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.278174	1.231385	2.482652	8.344523	2.949118	10.575774	2.451735	11.827038	0.7298	0.7224	0.2011	5.336448	4.638694
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.595572	1.160270	2.873351	9.399678	0.117838	-0.039829	-0.747398	-0.369274	0.6014	0.5986	0.3649	0.000000	0.000000
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	133213.299939	176678.033537	3975161.137843	2933821.074001	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	6.36%	0.338082	-1.101036	-0.304784	-4.266284	-1.214052	-2.084892	-1.500497	0.085062	0.6263	0.6283	0.3474	2.271116	1.829825
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.757788	0.842955	17.607697	9.378994	9.205975	4.119896	-1.473412	0.988282	0.6103	0.6128	0.3570	1.884494	1.971888
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.286586	-1.244119	-0.604612	-2.556831	-1.962162	-1.860760	-1.220821	-0.650669	0.6175	0.6251	0.3521	1.645476	1.520162
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	11.89%	0.352241	-1.016611	-0.000863	-2.128332	-1.268658	-1.822965	-2.005666	-1.497221	0.6262	0.6364	0.3515	1.434850	1.395444
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	4.32%	-0.100862	-1.069298	-0.431962	-2.306196	-0.960408	-1.431891	-1.333514	-0.944541	0.6038	0.6093	0.3663	1.489795	1.385687
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	61.29%	0.281213	-1.079129	0.052038	-1.924377	-0.622021	-1.961369	-1.384452	-0.733142	0.6260	0.6347	0.3422	2.885256	2.712557
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459557	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7601	0.7621	0.3483	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.990293	0.964379	9.907332	4.610407	8.609301	2.227125	-3.166116	-1.573451	0.6286	0.6309	0.3425	5.648252	4.130019
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6115	0.6138	0.3579	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6253	0.6279	0.3500	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.501666	1.175567	12.769830	7.110792	12.641331	4.084070	-0.208316	-0.705388	0.6369	0.6368	0.3494	9.933660	10.041286
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	38.71%	0.051131	-0.881532	-0.326383	-2.541673	-1.999778	-2.676232	-1.613969	-1.033389	0.6269	0.6288	0.3426	2.283886	1.895454
2459550	digital_ok	-	38.71%	38.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6254	0.6400	0.4109	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.547180	0.827022	9.377723	4.896974	8.957769	4.285752	-1.919335	-0.458192	0.6280	0.6304	0.3528	3.200110	2.762893
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.522983	1.055270	18.768851	10.511985	-0.030869	-0.267144	-1.913161	-1.259694	0.6886	0.6883	0.3220	4.567796	3.523666
2459541	digital_ok	100.00%	1.34%	1.34%	0.00%	100.00%	0.00%	3.079385	0.891552	11.555808	5.803896	7.094474	4.552638	3.313478	1.829419	0.6933	0.6881	0.2964	2.555149	2.316605
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	37.95%	1.406992	-0.277114	1.371734	-0.995086	-0.650839	-2.791113	-1.122928	-1.179450	0.6946	0.6882	0.3173	1.988978	1.805500
2459536	digital_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	4.733381	1.510214	15.236763	8.572305	13.164233	7.317326	-2.882168	-1.474989	0.4108	0.4072	0.2192	2.297596	2.003349
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.778248	-0.566323	0.934633	-0.996896	-0.926434	-2.558111	-1.226430	-0.692363	0.0511	0.0455	0.0053	1.202347	1.208735
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.238772	-0.720381	0.090572	-1.832325	-1.410490	-2.283505	-1.403717	-0.603540	0.0525	0.0477	0.0052	1.186928	1.193581
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.056729	0.915370	9.925237	5.127806	8.101739	3.243797	-2.267788	-1.306438	0.0471	0.0404	0.0027	1.198196	1.203147
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.754302	1.272387	12.924144	6.582415	9.416275	4.720723	-1.926857	-1.098487	0.0415	0.0387	0.0025	1.171892	1.178762
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.010082	-1.007364	-0.105639	-1.919840	-1.764505	-2.596889	-1.616679	1.195229	0.0474	0.0434	0.0050	1.205601	1.211995
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.534029	1.009278	10.799799	5.805570	19.155751	8.176265	-1.782503	0.323794	0.9072	0.9354	0.2953	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	4.347653	1.400456	13.825712	7.795565	4.736816	6.318180	-4.161047	-2.408445	nan	nan	nan	5.566075	4.832278
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.138976	1.057991	12.843893	6.602979	2.735721	4.724647	-2.487236	0.033559	0.7142	0.6781	0.3334	3.640673	3.602358
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	5.057139	5.358780	28.586458	26.707050	15.281119	38.553599	3.231017	17.677835	0.0462	0.0618	0.0046	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.717717	2.289878	8.716029	4.067879	8.557072	2.957325	7.725969	4.126083	0.0512	0.0413	0.0020	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	62.332001	102.797387	99.203673	115.319835	59.857432	147.203729	377.550618	910.581102	0.0078	0.0057	0.0033	1.095706	1.086673

Antenna Season Report Notebook¶

Antenna 40 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 40, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 40: 2459595

Antenna 40: 2459594

Antenna 40: 2459593

Antenna 40: 2459592

Antenna 40: 2459591

Antenna 40: 2459590

Antenna 40: 2459589

Antenna 40: 2459588

Antenna 40: 2459587

Antenna 40: 2459586

Antenna 40: 2459585

Antenna 40: 2459584

Antenna 40: 2459583

Antenna 40: 2459582

Antenna 40: 2459581

Antenna 40: 2459580

Antenna 40: 2459579

Antenna 40: 2459578

Antenna 40: 2459577

Antenna 40: 2459576

Antenna 40: 2459575

Antenna 40: 2459574

Antenna 40: 2459573

Antenna 40: 2459572

Antenna 40: 2459571

Antenna 40: 2459570

Antenna 40: 2459569

Antenna 40: 2459566

Antenna 40: 2459565

Antenna 40: 2459564

Antenna 40: 2459563

Antenna 40: 2459562

Antenna 40: 2459561

Antenna 40: 2459560

Antenna 40: 2459559

Antenna 40: 2459558

Antenna 40: 2459556

Antenna 40: 2459552

Antenna 40: 2459551

Antenna 40: 2459549

Antenna 40: 2459542

Antenna 40: 2459541

Antenna 40: 2459540

Antenna 40: 2459536

Antenna 40: 2459535

Antenna 40: 2459534

Antenna 40: 2459533

Antenna 40: 2459532

Antenna 40: 2459530

Antenna 40: 2459527

Antenna 40: 2459524

Antenna 40: 2459523

Antenna 40: 2459522

Antenna 40: 2459513

Antenna 40: 2459508

Antenna 40: 2459506

Antenna 40: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶